We find the orignal information for this project on Amber’s website here.
1 Introduction
This is a report and analysis on Seattle’s bicycle sharing trends. The data includes weather reports for the area, the station locations, as well as trips taken by cycle riders. Future explorations of this type of data could include investigating more extensively the usage and effectiveness of other public transportation for this area. This would serve to establish a better understanding of public transportation trends for Seattle. Interestingly enough, this transportation company (Pronto Cycle Sharing) has since dissolved and the data points that were collected from this company are from October 2014-August2016. Thus, the trends showcased by this dataset are not necessarily reflective of current bike sharing trends in Seattle.
1.1 Loading the Necessary Data
Data was downloaded and compiled from Kaggle . csv files for “station,” “trip,” and “weather” from the company “Pronto Cycle Sharing” were pulled from this site and used for this project.
2 Station Locations
Look how widespread the rental stations are all over Seattle (see Figure 2.1)!
n_distinct(station_id)
1 58
Figure 2.1: Seattle Bike Station Locations
2.1 Locations (zoomed)
For optimal viewing, here are the station whereabouts with some zoom for location precision.
Figure 2.2: Closeup
Look at all those stations! Its hard to believe this company managed to go out of business!
2.2 Current Dock Count
Figure 2.3: Station Bike Counts
Quite a few bikes to choose from (as of August 2016)!
2.3 Change in Number of Bike Docks Per Station
Figure 2.4: Bikes Per Station
11 stations lost bike docks, 39 docks stayed the same, 8 stations gained docks.
3 Number of Rides Per Day
Figure 3.1: Daily Riders
People really like going on fall and spring rides multiple times a day. Can you blame them though, Seattle in the fall is remarkable!!
4 Plotting trips per month (by season; excluding summer)
Figure 4.1: Trips by season, per month
December, January, and February are coded as “Winter,” March, April, and May are coded as “Spring,” and September, October, and November are coded as “Fall.” It is pretty obvious that usage would peak in the mid-fall, decline going into the winter months, and start a gradual increase as it transitions from winter into spring. Spring is the ultimate peak of bike riding peaking about early to mid-March.
5 Average Trip Duration
Figure 5.1: Conversions
Not a huge amount of variance between the seasons and the amount of time spent on the average ride. This could also be due to the sheer expense of riding with Pronto in general for both members and nonmembers.
6 Number of Trips by Day of Week
Figure 6.1: Weekday Trips
Oddly enough, there is no day of the week that is consistently higher (even on the weekends). Obviously the summer has the highest level of riders, but otherwise, no a lot of distint trends from this plot.
7 Number of Trips Per Time of Day
Figure 7.1: Daily Trip Log
People seem to most commonly use this service during the peak commuting hours (i.e. 8am and 5pm). However, on the weekends, the peak is around mid-day.
8 Number of Trips by Member Type
Figure 8.1: Trips of Members v. Short-Term Pass Holder
As to be expected, members have much overall higher usage rates than short-term pass holders (24 hour pass or a 3 day pass are the two options for short-term). Short-term passes are likely most appropriate for tourists or people who are vacationing in Seattle.
The two plots almost mimic one another as far as usuage rates go - this is interesting because of the differentials between members and short-term passholders.
9 Trip Duration by Member Type
Figure 9.1: Members v. Short-Term Pass Holder
9.1 Cost of a Biking Trip
Figure 9.2: Trip Cost for Members v. Non-Members
It is definitely worth it to be a member vs. a nonmember, there are many associated costs if you aren’t.(see Figure 9.2)!
10 Bicycle Sharing Member Demographics
Figure 10.1: Member Ages
Riders tend to be aged towards the upper 20s, but this number could be misleading because we don’t have any real identifiers for the users (i.e. is one 20 year old taking 100 trips a day, or if there are a ton of 20 year olds using the service one time).
11 Temperature Variance In Seattle
11.1 Minimum Temperatures
[1] "2016-02-14"
11.2 Mean Temperatures
11.3 Maximum Temperatures
12 Local Weather Events
What a rainy city!
13 Mean Temperature vs. Number of Trips
13.1 Normalize the Temperature vs. Number of Trips
14 Precipitation vs. Number of Trips
